A universal statistical test for random bit generators
Journal of Cryptology
Digital Image Processing
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Attacks on Steganographic Systems
IH '99 Proceedings of the Third International Workshop on Information Hiding
Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Consistent Line Clusters for Building Recognition in CBIR
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
A Computationally Efficient Approach to Indoor/Outdoor Scene Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Weighting for Bag-of-Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Reliable detection of LSB steganography in color and grayscale images
MM&Sec '01 Proceedings of the 2001 workshop on Multimedia and security: new challenges
The ultimate steganalysis benchmark?
Proceedings of the 9th workshop on Multimedia & security
Distinguishing paintings from photographs
Computer Vision and Image Understanding
Indoor vs. outdoor scene classification in digital photographs
Pattern Recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
How realistic is photorealistic?
IEEE Transactions on Signal Processing
Vision of the unseen: Current trends and challenges in digital image and video forensics
ACM Computing Surveys (CSUR)
Journal of Visual Communication and Image Representation
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In this paper, we introduce the progressive randomization (PR): a new image meta-description approach suitable for different image inference applications such as broad class Image Categorization, Forensics and Steganalysis. The main difference among PR and the state-of-the-art algorithms is that it is based on progressive perturbations on pixel values of images. With such perturbations, PR captures the image class separability allowing us to successfully infer high-level information about images. Even when only a limited number of training examples are available, the method still achieves good separability, and its accuracy increases with the size of the training set. We validate the method using two different inference scenarios and four image databases.